Dokumen ini ditujukan untuk arsitek, developer, dan administrator yang
merencanakan, mendesain, men-deploy, dan mengelola workload di Google Cloud.
Rekomendasi dalam pilar ini dapat membantu organisasi Anda beroperasi secara efisien, meningkatkan kepuasan pelanggan, meningkatkan pendapatan, dan mengurangi biaya.
Misalnya, saat waktu pemrosesan backend aplikasi menurun, pengguna akan mengalami respons yang lebih cepat, yang dapat menyebabkan retensi pengguna yang lebih tinggi dan peningkatan pendapatan.
Proses pengoptimalan performa dapat melibatkan kompromi antara performa dan biaya. Namun, mengoptimalkan performa terkadang dapat membantu Anda mengurangi biaya. Misalnya, saat beban meningkat, penskalaan otomatis dapat membantu
memberikan performa yang dapat diprediksi dengan memastikan bahwa resource sistem tidak
kelebihan beban. Penskalaan otomatis juga membantu Anda mengurangi biaya dengan menghapus resource yang tidak digunakan selama periode beban rendah.
Pengoptimalan performa adalah proses berkelanjutan, bukan aktivitas satu kali. Diagram
berikut ini menampilkan tahapan dalam proses pengoptimalan performa:
Proses pengoptimalan performa adalah siklus berkelanjutan yang mencakup
tahapan berikut:
Tentukan persyaratan: Tentukan persyaratan performa terperinci untuk setiap lapisan stack aplikasi sebelum Anda mendesain dan mengembangkan aplikasi. Untuk merencanakan alokasi resource, pertimbangkan karakteristik workload utama dan ekspektasi performa.
Desain dan deployment: Gunakan pola desain yang elastis dan skalabel yang dapat membantu Anda memenuhi persyaratan performa.
Pantau dan analisis: Pantau performa secara berkelanjutan menggunakan log, pelacakan, metrik, dan pemberitahuan.
Optimalkan: Pertimbangkan desain ulang yang mungkin diperlukan seiring perkembangan aplikasi Anda.
Menyesuaikan ukuran resource cloud dan menggunakan fitur baru untuk memenuhi persyaratan performa yang berubah.
Seperti yang ditunjukkan pada diagram sebelumnya, lanjutkan siklus pemantauan,
penilaian ulang persyaratan, dan penyesuaian resource cloud.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2024-12-06 UTC."],[[["\u003cp\u003eThis document, part of the Google Cloud Well-Architected Framework, offers guidance on optimizing the performance of workloads in Google Cloud for architects, developers, and administrators.\u003c/p\u003e\n"],["\u003cp\u003ePerformance optimization is an ongoing process that includes defining requirements, designing and deploying, monitoring and analyzing, and optimizing resources in a continuous cycle.\u003c/p\u003e\n"],["\u003cp\u003eThe core principles of performance optimization in this framework include planning resource allocation, taking advantage of elasticity, promoting modular design, and continuously monitoring and improving performance.\u003c/p\u003e\n"],["\u003cp\u003eOptimizing performance can lead to improved operational efficiency, enhanced customer satisfaction, increased revenue, and reduced costs, with potential trade-offs between performance and cost.\u003c/p\u003e\n"],["\u003cp\u003eThere is a guide available for AI and ML specific performance optimization, in the AI and ML perspective of the Well-Architected Framework.\u003c/p\u003e\n"]]],[],null,["# Well-Architected Framework: Performance optimization pillar\n\n| To view the content in the performance optimization pillar on a single page or to to get a PDF output of the content, see [View on one page](/architecture/framework/performance-optimization/printable).\n\nThis pillar in the\n[Google Cloud Well-Architected Framework](/architecture/framework)\nprovides recommendations to optimize the performance of workloads in\nGoogle Cloud.\n\nThis document is intended for architects, developers, and administrators who\nplan, design, deploy, and manage workloads in Google Cloud.\n\nThe recommendations in this pillar can help your organization to operate\nefficiently, improve customer satisfaction, increase revenue, and reduce cost.\nFor example, when the backend processing time of an application decreases, users\nexperience faster response times, which can lead to higher user retention and\nmore revenue.\n\nThe performance optimization process can involve a trade-off between\nperformance and cost. However, optimizing performance can sometimes help you\nreduce costs. For example, when the load increases, autoscaling can help to\nprovide predictable performance by ensuring that the system resources aren't\noverloaded. Autoscaling also helps you to reduce costs by removing unused\nresources during periods of low load.\n\nPerformance optimization is a continuous process, not a one-time activity. The\nfollowing diagram shows the stages in the performance optimization process:\n\nThe performance optimization process is an ongoing cycle that includes the\nfollowing stages:\n\n1. **Define requirements**: Define granular performance requirements for each layer of the application stack before you design and develop your applications. To plan resource allocation, consider the key workload characteristics and performance expectations.\n2. **Design and deploy**: Use elastic and scalable design patterns that can help you meet your performance requirements.\n3. **Monitor and analyze**: Monitor performance continually by using logs, tracing, metrics, and alerts.\n4. **Optimize**: Consider potential redesigns as your applications evolve.\n Rightsize cloud resources and use new features to meet changing performance\n requirements.\n\n As shown in the preceding diagram, continue the cycle of monitoring,\n re-assessing requirements, and adjusting the cloud resources.\n\n\nFor performance optimization principles and recommendations that are specific to AI and ML workloads, see\n[AI and ML perspective: Performance optimization](/architecture/framework/perspectives/ai-ml/performance-optimization)\nin the Well-Architected Framework.\n\nCore principles\n---------------\n\nThe recommendations in the performance optimization pillar of the Well-Architected Framework\nare mapped to the following core principles:\n\n- [Plan resource allocation](/architecture/framework/performance-optimization/plan-resource-allocation)\n- [Take advantage of elasticity](/architecture/framework/performance-optimization/elasticity)\n- [Promote modular design](/architecture/framework/performance-optimization/promote-modular-design)\n- [Continuously monitor and improve performance](/architecture/framework/performance-optimization/continuously-monitor-and-improve-performance)\n\nContributors\n------------\n\nAuthors:\n\n- [Daniel Lees](https://www.linkedin.com/in/daniellees) \\| Cloud Security Architect\n- [Gary Harmson](https://www.linkedin.com/in/garyharmson) \\| Principal Architect\n- [Luis Urena](https://www.linkedin.com/in/urena-luis) \\| Developer Relations Engineer\n- [Zach Seils](https://www.linkedin.com/in/zachseils) \\| Networking Specialist\n\n\u003cbr /\u003e\n\nOther contributors:\n\n- [Filipe Gracio, PhD](https://www.linkedin.com/in/filipegracio) \\| Customer Engineer, AI/ML Specialist\n- [Jose Andrade](https://www.linkedin.com/in/jmandrade) \\| Customer Engineer, SRE Specialist\n- [Kumar Dhanagopal](https://www.linkedin.com/in/kumardhanagopal) \\| Cross-Product Solution Developer\n- [Marwan Al Shawi](https://www.linkedin.com/in/marwanalshawi) \\| Partner Customer Engineer\n- [Nicolas Pintaux](https://www.linkedin.com/in/nicolaspintaux) \\| Customer Engineer, Application Modernization Specialist\n- [Ryan Cox](https://www.linkedin.com/in/ryanlcox) \\| Principal Architect\n- [Radhika Kanakam](https://www.linkedin.com/in/radhika-kanakam-18ab876) \\| Program Lead, Google Cloud Well-Architected Framework\n- [Samantha He](https://www.linkedin.com/in/samantha-he-05a98173) \\| Technical Writer\n- [Wade Holmes](https://www.linkedin.com/in/wholmes) \\| Global Solutions Director\n\n\u003cbr /\u003e"]]
